diff --git a/sam3/model_builder.py b/sam3/model_builder.py
index 103b324..7662b68 100644
--- a/sam3/model_builder.py
+++ b/sam3/model_builder.py
@@ -561,8 +561,8 @@ def build_sam3_image_model(
     bpe_path=None,
     device="cuda" if torch.cuda.is_available() else "cpu",
     eval_mode=True,
-    checkpoint_path=None,
-    load_from_HF=True,
+    checkpoint_path="/home/quant/data/dev/sam3/sam3.pt",
+    load_from_HF=False,
     enable_segmentation=True,
     enable_inst_interactivity=False,
     compile=False,
@@ -651,8 +651,8 @@ def download_ckpt_from_hf():
 
 
 def build_sam3_video_model(
-    checkpoint_path: Optional[str] = None,
-    load_from_HF=True,
+    checkpoint_path: Optional[str] = "/home/quant/data/dev/sam3/sam3.pt",
+    load_from_HF=False,
     bpe_path: Optional[str] = None,
     has_presence_token: bool = True,
     geo_encoder_use_img_cross_attn: bool = True,
diff --git a/test.py b/test.py
new file mode 100644
index 0000000..0b39bb1
--- /dev/null
+++ b/test.py
@@ -0,0 +1,39 @@
+import torch
+#################################### For Image ####################################
+from PIL import Image
+from sam3.model_builder import build_sam3_image_model
+from sam3.model.sam3_image_processor import Sam3Processor
+# Load the model
+model = build_sam3_image_model()
+processor = Sam3Processor(model)
+# Load an image
+image = Image.open("/home/quant/data/dev/sam3-main/assets/player.gif")
+inference_state = processor.set_image(image)
+# Prompt the model with text
+output = processor.set_text_prompt(state=inference_state, prompt="pepole")
+
+# Get the masks, bounding boxes, and scores
+masks, boxes, scores = output["masks"], output["boxes"], output["scores"]
+
+#################################### For Video ####################################
+
+# from sam3.model_builder import build_sam3_video_predictor
+
+# video_predictor = build_sam3_video_predictor()
+# video_path = "<YOUR_VIDEO_PATH>" # a JPEG folder or an MP4 video file
+# # Start a session
+# response = video_predictor.handle_request(
+#     request=dict(
+#         type="start_session",
+#         resource_path=video_path,
+#     )
+# )
+# response = video_predictor.handle_request(
+#     request=dict(
+#         type="add_prompt",
+#         session_id=response["session_id"],
+#         frame_index=0, # Arbitrary frame index
+#         text="<YOUR_TEXT_PROMPT>",
+#     )
+# )
+# output = response["outputs"]
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